hyperSPACE:Automated Optimization of Complex Processing Pipelines for pySPACE
In NIPS Workshop on Bayesian Optimization, (BayesOPT2016), 05.12.-10.12.2016, Barcelona, n.n., Dec/2016.
Even though more and more algorithms are introduced for optimizing hyperparameters
and complex processing pipelines, it still remains a cumbersome task for
domain experts. Grid search is still used in most cases despite its deficiencies. In
this paper, we combine the optimization library Hyperopt with the signal processing
and classification environment pySPACE to completely automatize the optimization
process. Even though no preliminary knowledge is required, interfaces for
domain and algorithm experts were added to accelerate the optimization. As a
proof of concept, the new framework is applied to electroencephalographic data
which requires exhaustive optimization of a complex processing pipeline.